hyperactive.optimizers.objective_function.ObjectiveFunction.__init__() - Code Metrics - Inspection of "Merge branch 'master' of https://github.com/SimonB..." - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

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by Simon

created 2022-05-03 10:57 UTC

ObjectiveFunction.init() A

↳ Parent: hyperactive.optimizers.objective_function

Complexity

Conditions

Size

Total Lines	10
Code Lines	8

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	8
nop	6
dl	0
loc	10
rs	10
c	0
b	0
f	0

# Author: Simon Blanke
# Email: [email protected]
# License: MIT License


import numpy as np
from .dictionary import DictClass


def gfo2hyper(search_space, para):
    values_dict = {}
    for _, key in enumerate(search_space.keys()):
        pos_ = int(para[key])
        values_dict[key] = search_space[key][pos_]

    return values_dict


class ObjectiveFunction(DictClass):
    def __init__(self, objective_function, optimizer, callbacks, catch, nth_process):
        super().__init__()

        self.objective_function = objective_function
        self.optimizer = optimizer
        self.callbacks = callbacks
        self.catch = catch
        self.nth_process = nth_process

        self.nth_iter = 0

    def run_callbacks(self, type_):
        if self.callbacks and type_ in self.callbacks:
            [callback(self) for callback in self.callbacks[type_]]

    def __call__(self, search_space):
        # wrapper for GFOs
        def _model(para):
            self.nth_iter = len(self.optimizer.pos_l)
            para = gfo2hyper(search_space, para)
            self.para_dict = para

            try:
                self.run_callbacks("before")
                results = self.objective_function(self)
                self.run_callbacks("after")
            except tuple(self.catch.keys()) as e:
                results = self.catch[e.__class__]

            return results

        _model.__name__ = self.objective_function.__name__
        return _model


1			# Author: Simon Blanke
2			# Email: [email protected]
3			# License: MIT License
4
5
6			import numpy as np
7			from .dictionary import DictClass
8
9
10			def gfo2hyper(search_space, para):
11			values_dict = {}
12			for _, key in enumerate(search_space.keys()):
13			pos_ = int(para[key])
14			values_dict[key] = search_space[key][pos_]
15
16			return values_dict
17
18
19			class ObjectiveFunction(DictClass):
20			def __init__(self, objective_function, optimizer, callbacks, catch, nth_process):
21			super().__init__()
22
23			self.objective_function = objective_function
24			self.optimizer = optimizer
25			self.callbacks = callbacks
26			self.catch = catch
27			self.nth_process = nth_process
28
29			self.nth_iter = 0
30
31			def run_callbacks(self, type_):
32			if self.callbacks and type_ in self.callbacks:
33			[callback(self) for callback in self.callbacks[type_]]
34
35			def __call__(self, search_space):
36			# wrapper for GFOs
37			def _model(para):
38			self.nth_iter = len(self.optimizer.pos_l)
39			para = gfo2hyper(search_space, para)
40			self.para_dict = para
41
42			try:
43			self.run_callbacks("before")
44			results = self.objective_function(self)
45			self.run_callbacks("after")
46			except tuple(self.catch.keys()) as e:
47			results = self.catch[e.__class__]
48
49			return results
50
51			_model.__name__ = self.objective_function.__name__
52			return _model
53

SimonBlanke / Hyperactive

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ObjectiveFunction.__init__() A

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